Wavelet-based detection of transcriptional activity on a novel <it>Staphylococcus aureus</it> tiling microarray
<p>Abstract</p> <p>Background</p> <p>High-density oligonucleotide microarray is an appropriate technology for genomic analysis, and is particulary useful in the generation of transcriptional maps, ChIP-on-chip studies and re-sequencing of the genome.Transcriptome analys...
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doaj-aaabe8a84ea64dd7b0ea3ad6d08e136a2020-11-24T20:54:28ZengBMCBMC Bioinformatics1471-21052012-09-0113122210.1186/1471-2105-13-222Wavelet-based detection of transcriptional activity on a novel <it>Staphylococcus aureus</it> tiling microarraySegura VíctorToledo-Arana AlejandroUzqueda MaiteLasa IñigoMuñoz-Barrutia Arrate<p>Abstract</p> <p>Background</p> <p>High-density oligonucleotide microarray is an appropriate technology for genomic analysis, and is particulary useful in the generation of transcriptional maps, ChIP-on-chip studies and re-sequencing of the genome.Transcriptome analysis of tiling microarray data facilitates the discovery of novel transcripts and the assessment of differential expression in diverse experimental conditions. Although new technologies such as next-generation sequencing have appeared, microarrays might still be useful for the study of small genomes or for the analysis of genomic regions with custom microarrays due to their lower price and good accuracy in expression quantification.</p> <p>Results</p> <p>Here, we propose a novel wavelet-based method, named ZCL (zero-crossing lines), for the combined denoising and segmentation of tiling signals. The denoising is performed with the classical SUREshrink method and the detection of transcriptionally active regions is based on the computation of the Continuous Wavelet Transform (CWT). In particular, the detection of the transitions is implemented as the thresholding of the zero-crossing lines. The algorithm described has been applied to the public <it>Saccharomyces cerevisiae</it> dataset and it has been compared with two well-known algorithms: pseudo-median sliding window (PMSW) and the structural change model (SCM). As a proof-of-principle, we applied the ZCL algorithm to the analysis of the custom tiling microarray hybridization results of a <it>S. aureus</it> mutant deficient in the sigma B transcription factor. The challenge was to identify those transcripts whose expression decreases in the absence of sigma B.</p> <p>Conclusions</p> <p>The proposed method archives the best performance in terms of positive predictive value (PPV) while its sensitivity is similar to the other algorithms used for the comparison. The computation time needed to process the transcriptional signals is low as compared with model-based methods and in the same range to those based on the use of filters. Automatic parameter selection has been incorporated and moreover, it can be easily adapted to a parallel implementation. We can conclude that the proposed method is well suited for the analysis of tiling signals, in which transcriptional activity is often hidden in the noise. Finally, the quantification and differential expression analysis of <it>S. aureus</it> dataset have demonstrated the valuable utility of this novel device to the biological analysis of the <it>S. aureus</it> transcriptome.</p> http://www.biomedcentral.com/1471-2105/13/222 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Segura Víctor Toledo-Arana Alejandro Uzqueda Maite Lasa Iñigo Muñoz-Barrutia Arrate |
spellingShingle |
Segura Víctor Toledo-Arana Alejandro Uzqueda Maite Lasa Iñigo Muñoz-Barrutia Arrate Wavelet-based detection of transcriptional activity on a novel <it>Staphylococcus aureus</it> tiling microarray BMC Bioinformatics |
author_facet |
Segura Víctor Toledo-Arana Alejandro Uzqueda Maite Lasa Iñigo Muñoz-Barrutia Arrate |
author_sort |
Segura Víctor |
title |
Wavelet-based detection of transcriptional activity on a novel <it>Staphylococcus aureus</it> tiling microarray |
title_short |
Wavelet-based detection of transcriptional activity on a novel <it>Staphylococcus aureus</it> tiling microarray |
title_full |
Wavelet-based detection of transcriptional activity on a novel <it>Staphylococcus aureus</it> tiling microarray |
title_fullStr |
Wavelet-based detection of transcriptional activity on a novel <it>Staphylococcus aureus</it> tiling microarray |
title_full_unstemmed |
Wavelet-based detection of transcriptional activity on a novel <it>Staphylococcus aureus</it> tiling microarray |
title_sort |
wavelet-based detection of transcriptional activity on a novel <it>staphylococcus aureus</it> tiling microarray |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2012-09-01 |
description |
<p>Abstract</p> <p>Background</p> <p>High-density oligonucleotide microarray is an appropriate technology for genomic analysis, and is particulary useful in the generation of transcriptional maps, ChIP-on-chip studies and re-sequencing of the genome.Transcriptome analysis of tiling microarray data facilitates the discovery of novel transcripts and the assessment of differential expression in diverse experimental conditions. Although new technologies such as next-generation sequencing have appeared, microarrays might still be useful for the study of small genomes or for the analysis of genomic regions with custom microarrays due to their lower price and good accuracy in expression quantification.</p> <p>Results</p> <p>Here, we propose a novel wavelet-based method, named ZCL (zero-crossing lines), for the combined denoising and segmentation of tiling signals. The denoising is performed with the classical SUREshrink method and the detection of transcriptionally active regions is based on the computation of the Continuous Wavelet Transform (CWT). In particular, the detection of the transitions is implemented as the thresholding of the zero-crossing lines. The algorithm described has been applied to the public <it>Saccharomyces cerevisiae</it> dataset and it has been compared with two well-known algorithms: pseudo-median sliding window (PMSW) and the structural change model (SCM). As a proof-of-principle, we applied the ZCL algorithm to the analysis of the custom tiling microarray hybridization results of a <it>S. aureus</it> mutant deficient in the sigma B transcription factor. The challenge was to identify those transcripts whose expression decreases in the absence of sigma B.</p> <p>Conclusions</p> <p>The proposed method archives the best performance in terms of positive predictive value (PPV) while its sensitivity is similar to the other algorithms used for the comparison. The computation time needed to process the transcriptional signals is low as compared with model-based methods and in the same range to those based on the use of filters. Automatic parameter selection has been incorporated and moreover, it can be easily adapted to a parallel implementation. We can conclude that the proposed method is well suited for the analysis of tiling signals, in which transcriptional activity is often hidden in the noise. Finally, the quantification and differential expression analysis of <it>S. aureus</it> dataset have demonstrated the valuable utility of this novel device to the biological analysis of the <it>S. aureus</it> transcriptome.</p> |
url |
http://www.biomedcentral.com/1471-2105/13/222 |
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